In [1]:
import pandas as pd

import plotly.express as px
import plotly.graph_objects as go
import plotly.io as pio
import plotly.colors as colors
pio.templates.default = "plotly_white"

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[1], line 3
      1 import pandas as pd
----> 3 import plotly.express as px
      4 import plotly.graph_objects as go
      5 import plotly.io as pio

ModuleNotFoundError: No module named 'plotly'
In [2]:
!pip install plotly

Collecting plotly
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Requirement already satisfied: packaging in c:\users\pmani\appdata\local\programs\python\python313\lib\site-packages (from plotly) (25.0)
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[notice] A new release of pip is available: 25.2 -> 25.3
[notice] To update, run: python.exe -m pip install --upgrade pip
In [27]:
import pandas as pd

import plotly.express as px
import plotly.graph_objects as go
import plotly.io as pio
import plotly.colors as colors
pio.templates.default = "plotly_white"
pio.renderers.default = "notebook"
In [28]:
data = pd.read_csv("Sample - Superstore.csv" , encoding = 'latin-1')
In [29]:
data.head()
Out[29]:
Row ID Order ID Order Date Ship Date Ship Mode Customer ID Customer Name Segment Country City ... Postal Code Region Product ID Category Sub-Category Product Name Sales Quantity Discount Profit
0 1 CA-2016-152156 11/8/2016 11/11/2016 Second Class CG-12520 Claire Gute Consumer United States Henderson ... 42420 South FUR-BO-10001798 Furniture Bookcases Bush Somerset Collection Bookcase 261.9600 2 0.00 41.9136
1 2 CA-2016-152156 11/8/2016 11/11/2016 Second Class CG-12520 Claire Gute Consumer United States Henderson ... 42420 South FUR-CH-10000454 Furniture Chairs Hon Deluxe Fabric Upholstered Stacking Chairs,... 731.9400 3 0.00 219.5820
2 3 CA-2016-138688 6/12/2016 6/16/2016 Second Class DV-13045 Darrin Van Huff Corporate United States Los Angeles ... 90036 West OFF-LA-10000240 Office Supplies Labels Self-Adhesive Address Labels for Typewriters b... 14.6200 2 0.00 6.8714
3 4 US-2015-108966 10/11/2015 10/18/2015 Standard Class SO-20335 Sean O'Donnell Consumer United States Fort Lauderdale ... 33311 South FUR-TA-10000577 Furniture Tables Bretford CR4500 Series Slim Rectangular Table 957.5775 5 0.45 -383.0310
4 5 US-2015-108966 10/11/2015 10/18/2015 Standard Class SO-20335 Sean O'Donnell Consumer United States Fort Lauderdale ... 33311 South OFF-ST-10000760 Office Supplies Storage Eldon Fold 'N Roll Cart System 22.3680 2 0.20 2.5164

5 rows × 21 columns

In [30]:
data.describe()
Out[30]:
Row ID Postal Code Sales Quantity Discount Profit
count 9994.000000 9994.000000 9994.000000 9994.000000 9994.000000 9994.000000
mean 4997.500000 55190.379428 229.858001 3.789574 0.156203 28.656896
std 2885.163629 32063.693350 623.245101 2.225110 0.206452 234.260108
min 1.000000 1040.000000 0.444000 1.000000 0.000000 -6599.978000
25% 2499.250000 23223.000000 17.280000 2.000000 0.000000 1.728750
50% 4997.500000 56430.500000 54.490000 3.000000 0.200000 8.666500
75% 7495.750000 90008.000000 209.940000 5.000000 0.200000 29.364000
max 9994.000000 99301.000000 22638.480000 14.000000 0.800000 8399.976000
In [31]:
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 9994 entries, 0 to 9993
Data columns (total 21 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Row ID         9994 non-null   int64  
 1   Order ID       9994 non-null   object 
 2   Order Date     9994 non-null   object 
 3   Ship Date      9994 non-null   object 
 4   Ship Mode      9994 non-null   object 
 5   Customer ID    9994 non-null   object 
 6   Customer Name  9994 non-null   object 
 7   Segment        9994 non-null   object 
 8   Country        9994 non-null   object 
 9   City           9994 non-null   object 
 10  State          9994 non-null   object 
 11  Postal Code    9994 non-null   int64  
 12  Region         9994 non-null   object 
 13  Product ID     9994 non-null   object 
 14  Category       9994 non-null   object 
 15  Sub-Category   9994 non-null   object 
 16  Product Name   9994 non-null   object 
 17  Sales          9994 non-null   float64
 18  Quantity       9994 non-null   int64  
 19  Discount       9994 non-null   float64
 20  Profit         9994 non-null   float64
dtypes: float64(3), int64(3), object(15)
memory usage: 1.6+ MB

Converting date columns¶

In [32]:
data['Order Date'] = pd.to_datetime(data['Order Date'])
In [33]:
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 9994 entries, 0 to 9993
Data columns (total 21 columns):
 #   Column         Non-Null Count  Dtype         
---  ------         --------------  -----         
 0   Row ID         9994 non-null   int64         
 1   Order ID       9994 non-null   object        
 2   Order Date     9994 non-null   datetime64[ns]
 3   Ship Date      9994 non-null   object        
 4   Ship Mode      9994 non-null   object        
 5   Customer ID    9994 non-null   object        
 6   Customer Name  9994 non-null   object        
 7   Segment        9994 non-null   object        
 8   Country        9994 non-null   object        
 9   City           9994 non-null   object        
 10  State          9994 non-null   object        
 11  Postal Code    9994 non-null   int64         
 12  Region         9994 non-null   object        
 13  Product ID     9994 non-null   object        
 14  Category       9994 non-null   object        
 15  Sub-Category   9994 non-null   object        
 16  Product Name   9994 non-null   object        
 17  Sales          9994 non-null   float64       
 18  Quantity       9994 non-null   int64         
 19  Discount       9994 non-null   float64       
 20  Profit         9994 non-null   float64       
dtypes: datetime64[ns](1), float64(3), int64(3), object(14)
memory usage: 1.6+ MB
In [34]:
data['Ship Date'] = pd.to_datetime(data['Ship Date'])
In [35]:
data.head()
Out[35]:
Row ID Order ID Order Date Ship Date Ship Mode Customer ID Customer Name Segment Country City ... Postal Code Region Product ID Category Sub-Category Product Name Sales Quantity Discount Profit
0 1 CA-2016-152156 2016-11-08 2016-11-11 Second Class CG-12520 Claire Gute Consumer United States Henderson ... 42420 South FUR-BO-10001798 Furniture Bookcases Bush Somerset Collection Bookcase 261.9600 2 0.00 41.9136
1 2 CA-2016-152156 2016-11-08 2016-11-11 Second Class CG-12520 Claire Gute Consumer United States Henderson ... 42420 South FUR-CH-10000454 Furniture Chairs Hon Deluxe Fabric Upholstered Stacking Chairs,... 731.9400 3 0.00 219.5820
2 3 CA-2016-138688 2016-06-12 2016-06-16 Second Class DV-13045 Darrin Van Huff Corporate United States Los Angeles ... 90036 West OFF-LA-10000240 Office Supplies Labels Self-Adhesive Address Labels for Typewriters b... 14.6200 2 0.00 6.8714
3 4 US-2015-108966 2015-10-11 2015-10-18 Standard Class SO-20335 Sean O'Donnell Consumer United States Fort Lauderdale ... 33311 South FUR-TA-10000577 Furniture Tables Bretford CR4500 Series Slim Rectangular Table 957.5775 5 0.45 -383.0310
4 5 US-2015-108966 2015-10-11 2015-10-18 Standard Class SO-20335 Sean O'Donnell Consumer United States Fort Lauderdale ... 33311 South OFF-ST-10000760 Office Supplies Storage Eldon Fold 'N Roll Cart System 22.3680 2 0.20 2.5164

5 rows × 21 columns

In [36]:
data['Order Month'] = data['Order Date'].dt.month
data['Order Year'] = data['Order Date'].dt.year
data['Order Day of Week'] = data['Order Date'].dt.dayofweek
In [37]:
data.head()
Out[37]:
Row ID Order ID Order Date Ship Date Ship Mode Customer ID Customer Name Segment Country City ... Category Sub-Category Product Name Sales Quantity Discount Profit Order Month Order Year Order Day of Week
0 1 CA-2016-152156 2016-11-08 2016-11-11 Second Class CG-12520 Claire Gute Consumer United States Henderson ... Furniture Bookcases Bush Somerset Collection Bookcase 261.9600 2 0.00 41.9136 11 2016 1
1 2 CA-2016-152156 2016-11-08 2016-11-11 Second Class CG-12520 Claire Gute Consumer United States Henderson ... Furniture Chairs Hon Deluxe Fabric Upholstered Stacking Chairs,... 731.9400 3 0.00 219.5820 11 2016 1
2 3 CA-2016-138688 2016-06-12 2016-06-16 Second Class DV-13045 Darrin Van Huff Corporate United States Los Angeles ... Office Supplies Labels Self-Adhesive Address Labels for Typewriters b... 14.6200 2 0.00 6.8714 6 2016 6
3 4 US-2015-108966 2015-10-11 2015-10-18 Standard Class SO-20335 Sean O'Donnell Consumer United States Fort Lauderdale ... Furniture Tables Bretford CR4500 Series Slim Rectangular Table 957.5775 5 0.45 -383.0310 10 2015 6
4 5 US-2015-108966 2015-10-11 2015-10-18 Standard Class SO-20335 Sean O'Donnell Consumer United States Fort Lauderdale ... Office Supplies Storage Eldon Fold 'N Roll Cart System 22.3680 2 0.20 2.5164 10 2015 6

5 rows × 24 columns

Monthly Sales Analysis¶

In [38]:
sales_by_month = data.groupby('Order Month')['Sales'].sum().reset_index()
fig = px.line(sales_by_month,
              x = 'Order Month',
              y = 'Sales',
              title = 'Monthly Sales Analysis')
fig.show()

Sales by Category¶

In [39]:
sales_by_category = data.groupby('Category')['Sales'].sum().reset_index()
In [40]:
sales_by_category
Out[40]:
Category Sales
0 Furniture 741999.7953
1 Office Supplies 719047.0320
2 Technology 836154.0330
In [43]:
fig = px.pie(sales_by_category,
             values = 'Sales',
             names = 'Category',
             hole = 0.5,
             color_discrete_sequence = px.colors.qualitative.Pastel)
fig.update_traces(textposition='inside',textinfo='percent+label')
fig.update_layout(title_text='Sales Analysis by Category',title_font=dict(size=24))

fig.show()

Sales Analysis by Sub-Category¶

In [44]:
sales_by_subcategory = data.groupby('Sub-Category')['Sales'].sum().reset_index()
In [45]:
sales_by_subcategory
Out[45]:
Sub-Category Sales
0 Accessories 167380.3180
1 Appliances 107532.1610
2 Art 27118.7920
3 Binders 203412.7330
4 Bookcases 114879.9963
5 Chairs 328449.1030
6 Copiers 149528.0300
7 Envelopes 16476.4020
8 Fasteners 3024.2800
9 Furnishings 91705.1640
10 Labels 12486.3120
11 Machines 189238.6310
12 Paper 78479.2060
13 Phones 330007.0540
14 Storage 223843.6080
15 Supplies 46673.5380
16 Tables 206965.5320
In [46]:
fig = px.bar(sales_by_subcategory,
             x  = 'Sub-Category',
             y = 'Sales',
             title = "Sales analysis by sub category")
fig.show()

Monthly Profit Analysis¶

In [47]:
profit_by_month = data.groupby('Order Month')['Profit'].sum().reset_index()
In [48]:
profit_by_month
Out[48]:
Order Month Profit
0 1 9134.4461
1 2 10294.6107
2 3 28594.6872
3 4 11587.4363
4 5 22411.3078
5 6 21285.7954
6 7 13832.6648
7 8 21776.9384
8 9 36857.4753
9 10 31784.0413
10 11 35468.4265
11 12 43369.1919
In [49]:
fig = px.line(profit_by_month,
              x = 'Order Month',
              y = 'Profit',
              title = "Monthly profit analysis")
fig.show()

Profit by Category¶

In [50]:
profit_by_category = data.groupby('Category')['Profit'].sum().reset_index()
In [51]:
profit_by_category
Out[51]:
Category Profit
0 Furniture 18451.2728
1 Office Supplies 122490.8008
2 Technology 145454.9481
In [53]:
fig = px.pie(profit_by_category,
             values = 'Profit',
             names = 'Category',
             hole = 0.5,
             color_discrete_sequence = px.colors.qualitative.Pastel)

fig.update_traces(textposition = 'inside', textinfo = 'percent+label')
fig.update_layout(title_text = 'Profit Analysis by Category', title_font = dict(size=24))

fig.show()

Profit by Sub Category¶

In [54]:
profit_by_subcategory = data.groupby('Sub-Category')['Profit'].sum().reset_index()
fig = px.bar(profit_by_subcategory,
             x = 'Sub-Category',
             y = 'Profit',
             title = "Profit Analysis by Sub-Category")

fig.show()

Sales and Profit - Customer Segment¶

In [55]:
sales_profit_by_segment = data.groupby('Segment').agg({'Sales':'sum','Profit':'sum'}).reset_index()

color_palette = colors.qualitative.Pastel

fig = go.Figure()
fig.add_trace(go.Bar(x = sales_profit_by_segment['Segment'],
                     y = sales_profit_by_segment['Sales'],
                     name = 'Sales',
                     marker_color = color_palette[0]))

fig.add_trace(go.Bar(x = sales_profit_by_segment['Segment'],
                     y = sales_profit_by_segment['Profit'],
                     name = 'Profit',
                     marker_color = color_palette[1]))

fig.update_layout(title = 'Sales and Profit Analysis by Customer Segment',
                  xaxis_title = 'Customer_segment',yaxis_title = 'Amount')

fig.show()

Sales to Profit ratio¶

In [56]:
sales_profit_by_segment = data.groupby('Segment').agg({'Sales':'sum','Profit':'sum'}).reset_index()
sales_profit_by_segment['Sales_to_Profit_Ratio'] = sales_profit_by_segment['Sales']/sales_profit_by_segment['Profit']
print(sales_profit_by_segment[['Segment','Sales_to_Profit_Ratio']])

       Segment  Sales_to_Profit_Ratio
0     Consumer               8.659471
1    Corporate               7.677245
2  Home Office               7.125416
In [ ]: